A Method Based on Convex Cone Model for Image-Set Classification With CNN Features

In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as its input. CNN feature has non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a convex cone and measure the geometrical similarity of convex cones in classification. To achieve this framework, we define sequentially multiple angles between two convex cones by repeating the alternating least square method, and then define the geometrical similarity between the cones by using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, which maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, like Fisher discriminant analysis. Finally, the classification is conducted by measuring the similarity between projected convex cones. The effectiveness of the proposed method is demonstrated through evaluation experiments on a private database of a multi-view hand shape dataset, and two public databases.